CN107241590B - Image enhancement method and image enhancement device - Google Patents

Image enhancement method and image enhancement device Download PDF

Info

Publication number
CN107241590B
CN107241590B CN201710514728.XA CN201710514728A CN107241590B CN 107241590 B CN107241590 B CN 107241590B CN 201710514728 A CN201710514728 A CN 201710514728A CN 107241590 B CN107241590 B CN 107241590B
Authority
CN
China
Prior art keywords
image
color space
enhancement
parameter range
parameter
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Expired - Fee Related
Application number
CN201710514728.XA
Other languages
Chinese (zh)
Other versions
CN107241590A (en
Inventor
黄振诚
蔡宏奇
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
BenQ Intelligent Technology Shanghai Co Ltd
BenQ Corp
Original Assignee
BenQ Corp
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by BenQ Corp filed Critical BenQ Corp
Priority to CN201710514728.XA priority Critical patent/CN107241590B/en
Publication of CN107241590A publication Critical patent/CN107241590A/en
Application granted granted Critical
Publication of CN107241590B publication Critical patent/CN107241590B/en
Expired - Fee Related legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Images

Classifications

    • HELECTRICITY
    • H04ELECTRIC COMMUNICATION TECHNIQUE
    • H04NPICTORIAL COMMUNICATION, e.g. TELEVISION
    • H04N9/00Details of colour television systems
    • H04N9/64Circuits for processing colour signals
    • H04N9/646Circuits for processing colour signals for image enhancement, e.g. vertical detail restoration, cross-colour elimination, contour correction, chrominance trapping filters

Landscapes

  • Engineering & Computer Science (AREA)
  • Multimedia (AREA)
  • Signal Processing (AREA)
  • Image Processing (AREA)
  • Studio Devices (AREA)

Abstract

The invention provides an image enhancement method and an image enhancement device. The image enhancement device comprises a setting module, an analysis module and an enhancement module. The analysis module is coupled to the setting module and the enhancement module is coupled to the analysis module. The image enhancement method comprises the following steps: selecting a first color space to be operated by a setting module, and respectively setting a first parameter range of the first color space and a first image enhancement processing corresponding to the first parameter range; analyzing the content of the image by an analysis module according to the first color space, and judging whether the numerical value of at least one color parameter of the image area in the image is in a first parameter range or not; if the judgment result of the analysis module is yes, the enhancement module is used for carrying out first image enhancement processing on the image area. The image area with noise in the image is not judged as the image area to be subjected to image enhancement processing, so that the image area is prevented from being enhanced, and the image quality of the whole image can be effectively enhanced.

Description

Image enhancement method and image enhancement device
Technical Field
The present invention relates to the field of image processing, and in particular, to an image enhancement method and an image enhancement apparatus.
Background
Generally, no matter what the contrast of the image content displayed by the display device is in the color space, the conventional image enhancement method usually amplifies the contrast of the entire image content uniformly, resulting in white edges at the edges of the object displayed in the image. Therefore, the white edge phenomenon can be improved in practice by performing Detail enhancement (Detail enhancement) on the regions with higher spatial frequency and lower amplitude in the image.
However, Noise (Noise) exists in some unavoidable regions of the image, and once Noise occurs at a higher spatial frequency and a lower amplitude in the image, it is likely to initiate a mechanism of detail enhancement, so that the Noise is also enhanced, resulting in poor image quality. However, if the intensity of detail enhancement processing is reduced to avoid enhancing noise, the image quality of the image cannot be effectively enhanced, which is a dilemma to overcome.
Therefore, it is necessary to design a new image enhancement method and a new image enhancement apparatus to overcome the above-mentioned drawbacks.
Disclosure of Invention
The present invention provides an image enhancement method and an image enhancement apparatus, which are used to further perform detail enhancement processing on a specific image area to be enhanced in an image that has undergone image processing, so as to increase the sharpness of the specific image area, and also eliminate an image area that is not required to be enhanced and possibly generates noise, so as to avoid performing enhancement processing on the noise, and thus, the image quality of the image can be effectively improved.
According to an embodiment of the present invention, an image enhancement method is provided, which includes the following steps: (a) selecting a first color space to be operated; (b) setting a first parameter range of the first color space and a first image enhancement processing corresponding to the first parameter range; (c) analyzing the content of the image according to the first color space, and judging whether the value of at least one color parameter of the image area in the image falls within the first parameter range or not; and (d) if the judgment result in the step (c) is positive, performing the first image enhancement processing on the image area.
As an optional technical solution, when the determination result in the step (c) is yes, the step (d) may first determine whether the spatial frequency and the amplitude corresponding to the image region satisfy a preset condition, and if so, the step (d) may perform the first image enhancement processing on the image region.
As an optional technical solution, the preset condition is that the spatial frequency is greater than a preset spatial frequency and the amplitude is smaller than a preset amplitude.
As an optional technical solution, the first color space is an HSG color space, an HSI color space, a YUV color space, an RGB color space, or an XYZ color space.
As an optional technical solution, the first image enhancement processing performs detail enhancement processing on the image area to increase the sharpness of the image area.
As an optional technical solution, the step (b) further includes setting a second parameter range of the first color space and a second image enhancement process corresponding to the second parameter range, where the second image enhancement process is different from the first image enhancement process.
As an optional technical solution, the method further comprises the sub-step of: (e) if the judgment result in the step (c) is negative, judging whether the value of the at least one color parameter of the image area is in the second parameter range; and (f) if the judgment result in the step (e) is positive, performing the second image enhancement processing on the image area.
As an optional technical solution, when the determination result in the step (e) is yes, the step (f) may first determine whether the spatial frequency and the amplitude corresponding to the image region satisfy a specific condition, and if so, perform the second image enhancement processing on the image region.
As an optional technical solution, the method further comprises the sub-step of: if the result of the step (e) is negative, it is determined that the image area does not need to be enhanced or has noise.
As an optional technical solution, the step (a) further includes selecting a second color space to be operated, and the step (b) further includes setting a third parameter range of the second color space and a third image enhancement processing corresponding to the third parameter range, the step (c) further includes analyzing the content of the image according to the second color space, and accordingly determining whether the value of the at least one color parameter of the image area in the image falls within the third parameter range, and if so, the step (d) further includes performing the third image enhancement processing on the image area.
As an optional technical solution, before the content analysis is performed on the image in the step (c), the image is subjected to image processing.
According to another embodiment of the present invention, an image enhancement apparatus is provided, comprising: the image enhancement device comprises a setting module, a processing module and a processing module, wherein the setting module is used for selecting a first color space to be operated and setting a first parameter range of the first color space and a first image enhancement processing corresponding to the first parameter range; the analysis module is coupled with the setting module and used for analyzing the content of the image according to the first color space and judging whether the value of at least one color parameter of the image area in the image falls within the first parameter range or not; and the enhancement module is coupled with the analysis module, and if the judgment result of the analysis module is yes, the enhancement module performs the first image enhancement processing on the image area.
As an optional technical solution, when the value of the at least one color parameter of the image area falls within the first parameter range, the analysis module first determines whether the spatial frequency and the amplitude corresponding to the image area satisfy preset conditions, and if so, the enhancement module performs the first image enhancement processing on the image area.
As an optional technical solution, the preset condition is that the spatial frequency is greater than a preset spatial frequency and the amplitude is smaller than a preset amplitude.
As an optional technical solution, the first color space is an HSG color space, an HSI color space, a YUV color space, an RGB color space, or an XYZ color space.
As an optional technical solution, the first image enhancement processing performs detail enhancement processing on the image area to increase the sharpness of the image area.
As an optional technical solution, the setting module further sets a second parameter range of the first color space and a second image enhancement process corresponding to the second parameter range, where the second image enhancement process is different from the first image enhancement process.
As an optional technical solution, if the determination result of the analysis module is negative, the analysis module further determines whether the value of the at least one color parameter of the image area falls within the second parameter range, and if so, the enhancement module performs the second image enhancement processing on the image area.
As an optional technical solution, when the value of the at least one color parameter of the image area falls within the second parameter range, the analysis module first determines whether the spatial frequency and the amplitude corresponding to the image area satisfy specific conditions, and if so, the enhancement module performs the second image enhancement processing on the image area.
As an optional technical solution, if the value of the at least one color parameter of the image area does not fall within the second parameter range, the analysis module determines that the image area does not need to be enhanced or has noise.
As an optional technical solution, the setting module further selects a second color space to be operated, and sets a third parameter range of the second color space and a third image enhancement processing corresponding to the third parameter range, the analyzing module further performs content analysis on the image according to the second color space, and accordingly determines whether a value of the at least one color parameter of the image area in the image falls within the third parameter range, if so, the enhancing module performs the third image enhancement processing on the image area.
As an optional technical solution, before the analysis module performs content analysis on the image, the image is subjected to image processing.
Compared with the prior art, the image area with noise in the image is not determined as the image area to be subjected to the image enhancement processing, so that the problem that the image quality of the whole image is deteriorated because the image area with noise in the prior art is also enhanced can be effectively avoided, the intensity of detail enhancement processing does not need to be reduced intentionally, and the image quality of the whole image can be effectively enhanced. In addition, the user can select a specific object type (such as leaves, wood grain or skin) in the image according to the preference or requirement, especially can perform image enhancement processing on the most important and most frequently noticed part in the image, and can also greatly increase the convenience and flexibility of the image enhancement processing.
Drawings
FIG. 1 is a flow chart of an image enhancement method according to a preferred embodiment of the present invention;
FIG. 2 is a schematic diagram illustrating a first image region R1-a third image region R3 in an image M;
FIG. 3 is a block diagram of an image enhancement device according to another preferred embodiment of the present invention.
Detailed Description
In order to further understand the objects, structures, features and functions of the present invention, the following embodiments are described in detail.
According to a preferred embodiment of the present invention, an image enhancement method is provided. In this embodiment, the object of the image enhancement method for image enhancement is an image that has been subjected to image processing, but the invention is not limited thereto.
It should be noted that, the image Enhancement method of the present invention only performs Detail Enhancement (Detail Enhancement) on the specific image area to be enhanced in the image, so as to increase the Sharpness (Sharpness) of the specific image area, and correspondingly exclude other image areas that do not need to be enhanced and may generate noise, thereby avoiding enhancing noise and effectively improving image quality.
Referring to fig. 1, fig. 1 is a flowchart illustrating an image enhancement method in this embodiment.
As shown in fig. 1, in step S10, the image enhancement method first selects a first color space to be operated. In practical applications, the image enhancement method can first read information about various color spaces from the database and provide a color space menu, so that a user can select a specific color space from a plurality of candidate color spaces listed in the color space menu as a first color space to be operated according to the user's needs or preferences. Of course, for convenience, the user can also directly use the default color space preset by the system as the first color space to be operated, and there is no specific limitation.
For example, in the color space menu shown in table one, the candidate color spaces that can be used as the first color space to be operated may include: (1) common color spaces such as HSG color space, (2) HSI color space, (3) YUV color space, (4) RGB color space, and (5) XYZ color space, may be any other color space not listed in the color space menu, and is not limited specifically.
Watch 1
Color space menu
(1) HSG color space
(2) HSI color spaceWorkshop
(3) YUV color space
(4) RGB color space
(5) XYZ color space
Next, in step S12, after the first color space to be operated is selected, the image enhancement method further sets a first parameter range of the first color space and a first image enhancement process corresponding to the first parameter range.
In practical applications, since the different types of color spaces have different color parameters, assuming that the user selects (1) the HSG color space from the color space menu of table one, the image enhancement method further reads the related information of the color parameters H, S and G of the HSG color space, such as the parameter ranges of the color parameters H, S and G corresponding to the different object types in the HSG color space, from the database, and provides an object type menu for the user to select the first object type to be image enhanced.
For example, in the object type menu shown in table two, the candidate object types of the first object type to be image enhanced include: (1) the object types include leaves, (2) wood grain, (3) skin, (4) stone, and (5) sand grain, but may be any other object types without any specific limitation.
Watch two
Object type menu
(1) Leaf of Chinese angelica
(2) Wood grain
(3) Skin(s)
(4) Stone (W.E.)
(5) Sand grain
Assuming that the user selects the first object type to be image-enhanced from the object type menu shown in table two as (1) leaf, the image enhancement method sets each color parameter (H, S, G) of the first color space (HSG color space) to have a first parameter range corresponding to the first object type (leaf) according to the first object type (leaf), for example, H is 100 to 125 degrees, S is 70 to 100 percent, and G is 30 to 50 percent, as shown in table three, but not limited thereto.
Therefore, if the color parameters H, S, and G of a certain image region in the image in the HSG color space fall within the first parameter range (H is 100 to 125 degrees, S is 70 to 100%, and G is 30 to 50%), the image enhancement method determines that the object represented by the image region should belong to the first object type (leaf) to be image-enhanced.
Watch III
Figure BDA0001336394120000081
In step S12, the image enhancement method further sets a first image enhancement process corresponding to the first parameter range, such as a detail enhancement process, but not limited thereto.
Next, in step S14, the image enhancement method performs content analysis on the image according to the selected first color space (e.g., but not limited to, HSG color space).
Then, in step S16, the image enhancement method determines whether the values of the color parameters (e.g., but not limited to, color parameters H, S and G of the HSG color space) of each image region in the image are within a first parameter range (e.g., but not limited to, parameter ranges of H, S and G are 100-125 degrees, 70-100% and 30-50%), respectively, according to the analysis result of the content analysis.
For example, as shown in fig. 2, assuming that the values (H1, S1, G1) of the color parameters H, S and G of the first image region R1 in the image M are (110 degrees, 80%, 40%), respectively, that is, the values (H1, S1, G1) of the color parameters H, S and G of the first image region R1 in the image M fall within the first parameter range (H is 100 to 125 degrees, S is 70 to 100%, G is 30 to 50%), the determination result of the step S16 for the first image region R1 should be that the object represented by the first image region R1 in the image M should belong to the first object type (leaf enhancement) to be performed, so in the step S18, the image enhancement method performs the corresponding first image enhancement processing on the first image region R1 in the image M, for example, performs the detail enhancement processing to increase the sharpness, but not limited thereto.
On the contrary, it is assumed that the values (H2, S2, G2) of the color parameters H, S and G of the second image region R2 in the image M are (70 degrees, 55%, 65%), respectively, that is, the values (H2, S2, G2) of the color parameters H, S and G of the second image region R2 in the image M do not fall within the first parameter range (H is 100 to 125 degrees, S is 70 to 100%, G is 30 to 50%), so the determination result in the step S16 for the second image region R2 should be no, which represents that the object represented by the second image region R2 in the image M does not belong to the first object type (leaf) to be image-enhanced, and therefore, the image enhancement method does not perform the first image enhancement processing on the second image region R2 in the image M.
It should be noted that, when the first image enhancement process is set as the detail enhancement process, since the detail enhancement process is more suitable for being used in the image region with higher spatial frequency and smaller amplitude, when the determination result of the step S16 for the first image region R1 is yes, in step S18, the image enhancement method may further determine whether the spatial frequency and the amplitude corresponding to the first image region R1 in the image M satisfy the predetermined condition, for example, the predetermined condition may be that the spatial frequency corresponding to the first image region R1 is greater than the predetermined spatial frequency and the corresponding amplitude is smaller than the predetermined amplitude, but not limited thereto.
If the determination result is that the first image region R1 in the representative image M is suitable for detail enhancement, the image enhancement method performs detail enhancement on the first image region R1 in the representative image M to increase the sharpness of the first image region R1 in the representative image M. If the determination result is negative, the first image region R1 in the representative image M should not be suitable for detail enhancement, so the image enhancement method does not perform detail enhancement on the first image region R1 in the image M, so as to avoid the occurrence of white edges at the object edges as in the prior art.
In practical applications, when the first color space is selected as the color space to be operated by the image enhancement method, the image enhancement method can be respectively set in a plurality of parameter ranges respectively corresponding to different object types in the first color space, and can further set different image enhancement processes respectively corresponding to the parameter ranges.
For example, in step S12, in addition to setting the first parameter range of the first color space (e.g., HSG color space, but not limited thereto) and the first image enhancement processing corresponding to the first parameter range, as shown in table four, the image enhancement method may further set the second parameter range of the first color space (e.g., the parameter range of H is 65-75 degrees; the parameter range of S is 50-65%; the parameter range of G is 60-80%, but not limited thereto) and the second image enhancement processing corresponding to the second parameter range according to the second object type (e.g., wood grain, but not limited thereto) to be image enhanced, and the second image enhancement processing is different from the first image enhancement processing.
Watch four
Figure BDA0001336394120000101
In the above example, if the determination result of the step S16 for the second image region R2 is no, the values (H2, S2, G2) of the color parameters H, S and G of the second image region R2 in the image M do not fall within the first parameter range, that is, the object represented by the second image region R2 in the image M should not belong to the first object type (leaf) to be image enhanced, at this time, the image enhancement method further determines whether the values (H2, S2, G2) of the color parameters H, S and G of the second image region R2 in the image M fall within the second parameter range (H65 to 75 degrees, S50 to 65%, G60 to 80%).
Since the color parameters H, S and G (H2, S2, and G2) of the second image region R2 in the image M are (70 degrees, 55%, and 65%), respectively, which fall within the second parameter range (H65 to 75 degrees, S50 to 65%, and G60 to 80%), the object represented by the second image region R2 in the representative image M should belong to the second object type (wood grain) to be image-enhanced.
If the second image enhancement process is a detail enhancement process, the image enhancement method further determines whether the spatial frequency and the amplitude corresponding to the second image region R2 in the image M satisfy specific conditions, for example, the specific conditions include that the spatial frequency is greater than a predetermined spatial frequency and the amplitude is less than a predetermined amplitude, but not limited thereto. If the determination result is yes, the second image region R2 in the representative image M should be suitable for performing the detail enhancement processing, so the image enhancement method performs the second image enhancement processing on the second image region R2 in the image M; if the determination result is negative, the second image region R2 in the representative image M should not be suitable for performing the detail enhancement processing, so the image enhancement method does not perform the second image enhancement processing on the second image region R2 in the image M.
In addition, assuming that the color parameters H, S and the values of G (H3, S3, and G3) of the third image region R3 in the image M are (10 degrees, 15%, and 95%), respectively, the color parameters H, S and the values of G (H3, S3, and G3) of the third image region R3 in the image M do not fall within the first parameter range (H is 100 to 125 degrees, S is 70 to 100%, and G is 30 to 50%) or the second parameter range (H is 65 to 75 degrees, S is 50 to 65%, and G is 60 to 80%), the image enhancement method determines the third image region R3 in the image M as an image region that does not need image enhancement processing.
It should be noted that, although the third image region R3 in the image M is noisy, as shown in fig. 2, the value of the color parameter of the noisy third image region R3 does not fall within the first and second parameter ranges corresponding to the first and second object types to be image enhanced, so that the noisy third image region R3 is not enhanced, and the disadvantage of the prior art that the noise is also enhanced can be effectively improved.
In practical applications, since each color space has different characteristics and advantages, the image enhancement method can also select a plurality of color spaces as the color space to be operated in step S10, and the image enhancement method can set at least one parameter range of the color spaces and the image enhancement processing corresponding to the at least one parameter range in step S12. When the first color space cannot effectively judge whether the image region needs to be subjected to image enhancement processing, the image enhancement method can also perform auxiliary judgment through the second color space (even the third color space).
For example, in step S10, the image enhancement method may further select a second color space (e.g., XYZ color space, but not limited thereto) to be operated; in step S12, the image enhancement method may further set a third parameter range (e.g., X-12, Y-66, Z-138) of the second color space and a third image enhancement process corresponding to the third parameter range, where the third image enhancement process is different from the first image enhancement process and the second image enhancement process. Similarly, the image enhancement method may set a fourth parameter range (for example, X119, Y28, and Z77) of the second color space and a fourth image enhancement process corresponding to the fourth parameter range, where the fourth image enhancement process is different from the first to third image enhancement processes.
Next, in step S14, the image enhancement method further analyzes the content of the image M according to a second color space (e.g., XYZ color space, but not limited thereto), and in step S16, further determines whether the values of the color parameters (X1, Y1, Z1) to (X3, Y3, Z3) in the first image region R1 to the third image region R3 of the image M fall within a third parameter range (or a fourth parameter range) according to the content analysis result of step S14. If the determination result is yes, in step S18, the image enhancement method performs a third image enhancement process (or a fourth image enhancement process) on the image area. If the result of the determination is negative, the image enhancement method does not perform the third image enhancement processing (or the fourth image enhancement processing) on the image area.
Another embodiment of the present invention is an image enhancement apparatus. In this embodiment, the image enhancement device is configured to further perform detail enhancement processing on a specific image area to be enhanced in an image that has undergone image processing, so as to increase the sharpness of the specific image area, and the image enhancement method can also exclude an image area that is not to be enhanced and is likely to generate noise, so as to avoid performing enhancement processing on the noise, and thus, the image quality of the image can be effectively improved.
Referring to fig. 3, fig. 3 is a functional block diagram of the image enhancement device in this embodiment. As shown in fig. 3, the image enhancement apparatus 1 includes a setting module 10, an analyzing module 12, and an enhancing module 14. The analysis module 12 is coupled to the setting module 10 and the enhancement module 14 is coupled to the analysis module 12.
In this embodiment, the setting module 10 is configured to select a first color space to be operated, and set a first parameter range PR1 of the first color space and a first image enhancement process EN1 corresponding to the first parameter range PR1 according to a type of an object to be image enhanced (such as, but not limited to, wood grain, leaf, or skin).
In practical applications, the setting module 10 can provide a color space menu to allow a user to select a certain color space from the color space menu as a first color space to be operated according to the requirement or preference of the user or directly preset the first color space by the system. In practice, the first color space may be an HSG color space, an HSI color space, a YUV color space, an RGB color space, an XYZ color space, or other color spaces, without any particular limitation.
After the user selects the first color space to be operated or selects the first color space preset by the system, the setting module 10 further needs to provide an object type menu for the user to select the first object type to be image enhanced, such as leaf, wood grain or stone. When the user selects the first object type to be image-enhanced, the setting module 10 further sets a first parameter range PR1 corresponding to the first object type and a first image enhancement process EN1 corresponding to the first parameter range PR1 in the first color space.
When the analysis module 12 receives the image M after image processing and the first parameter range PR1 of the first color space from the setting module 10, respectively, the analysis module 12 analyzes the content of the image M according to the first color space, and accordingly determines whether the value of at least one color parameter of each image area in the image M falls within the first parameter range PR 1.
If the determination result of the analysis module 12 is yes, the value representing the color parameter of the image region falls within the first parameter range PR1, so the analysis module 12 can determine that the image region should belong to the type of the object to be image-enhanced, and the enhancement module 14 performs the first image enhancement on the image region to output the image M' after image enhancement. In fact, the first image enhancement process can perform detail enhancement on the image area to increase the sharpness of the image area, but not limited thereto.
It should be noted that, when the value of the at least one color parameter of the image region falls within the first parameter range PR1, the analysis module 12 first determines whether the spatial frequency and the amplitude corresponding to the image region satisfy the predetermined condition. If the above determination result of the analysis module 12 is yes, the enhancement module 14 performs a first image enhancement process on the image area. In fact, the predetermined condition may be that the spatial frequency corresponding to the image region is greater than a predetermined spatial frequency and the amplitude corresponding to the image region is smaller than a predetermined amplitude, but not limited thereto.
It should be noted that, after the setting module 10 selects the first color space to be operated, the setting module 10 may further set other parameter ranges of the first color space and different image enhancement processes respectively corresponding to the parameter ranges, in addition to the first parameter range PR1 capable of setting the first color space and the first image enhancement process EN1 corresponding to the first parameter range PR 1.
In addition, the setting module 10 may further select another color space to be operated (e.g., a second color space) and set a third parameter range (and/or a fourth parameter range) of the second color space and a third image enhancement process (or a fourth image enhancement process) corresponding to the third parameter range (or the fourth parameter range), in addition to the first color space to be operated.
Then, the analyzing module 12 analyzes the content of the image according to the second color space, and accordingly determines whether the color parameter value of each image area in the image falls within a third parameter range (or a fourth parameter range), and if so, the enhancing module 14 performs a third image enhancement process (or a fourth image enhancement process) on the image area.
Compared with the prior art, the image enhancement method and the image enhancement device respectively compare the color parameters of each image area in the image after image processing with the color parameter ranges of different color spaces and corresponding to different object types stored in the database, so as to determine which image areas in the image have one or more object types needing image enhancement processing, and further identify the object types respectively corresponding to the image areas, so that the corresponding image enhancement processing can be respectively carried out on the image areas presenting different object types.
In the present invention, since the image area in which noise appears in the image is not determined as the image area to be subjected to the image enhancement processing, the problem of the image quality degradation of the whole image caused by the enhancement of the image area in which noise appears in the prior art can be effectively avoided, and the intensity of the detail enhancement processing does not need to be intentionally reduced, so that the image quality of the whole image can be effectively enhanced. In addition, the user can select a specific object type (such as leaves, wood grain or skin) in the image according to the preference or requirement, especially can perform image enhancement processing on the most important and most frequently noticed part in the image, and can also greatly increase the convenience and flexibility of the image enhancement processing.
The present invention has been described in relation to the above embodiments, which are only exemplary of the implementation of the present invention. It should be noted that the disclosed embodiments do not limit the scope of the invention. Rather, it is intended that all such modifications and variations be included within the spirit and scope of this invention.

Claims (16)

1. An image enhancement method, comprising the steps of:
(a) selecting a first color space and a second color space to be operated;
(b) setting a first parameter range of the first color space and a first image enhancement processing corresponding to the first parameter range; setting a third parameter range of the second color space and a third image enhancement process corresponding to the third parameter range;
(c) analyzing the content of the image according to the first color space, and judging whether the value of at least one color parameter of the image area in the image falls within the first parameter range or not; analyzing the content of the image according to the second color space, and accordingly judging whether the value of at least one color parameter of the image area in the image is within the third parameter range; and
(d) if the value of at least one color parameter of the image area corresponding to the first color space in the image is within the first parameter range, performing the first image enhancement processing on the image area corresponding to the first color space; if the value of at least one color parameter of the image area corresponding to the second color space in the image is within the third parameter range, performing the third image enhancement processing on the image area corresponding to the second color space;
if the result of the step (c) is yes, the step (d) first determines whether the spatial frequency and the amplitude corresponding to the image area satisfy that the spatial frequency is greater than a predetermined spatial frequency and the amplitude is smaller than a predetermined amplitude, and if so, the step (d) performs the first image enhancement on the image area.
2. The image enhancement method of claim 1, wherein the first color space is an HSG color space, an HSI color space, a YUV color space, an RGB color space or an XYZ color space.
3. The image enhancement method of claim 1, wherein the first image enhancement process performs detail enhancement on the image area to increase the sharpness of the image area.
4. The method of claim 1, wherein step (b) further comprises setting a second parameter range of the first color space and a second image enhancement process corresponding to the second parameter range, wherein the second image enhancement process is different from the first image enhancement process.
5. The image enhancement method according to claim 4, further comprising the step of:
(e) if the judgment result in the step (c) is negative, judging whether the value of the at least one color parameter of the image area is in the second parameter range; and
(f) if the judgment result in the step (e) is yes, the second image enhancement processing is carried out on the image area.
6. The image enhancement method of claim 5, wherein if the determination result in step (e) is yes, step (f) determines whether the spatial frequency and amplitude corresponding to the image region satisfy specific conditions, and if so, performs the second image enhancement on the image region.
7. The image enhancement method according to claim 5, further comprising the step of:
if the result of the step (e) is negative, it is determined that the image area does not need to be enhanced or has noise.
8. The method of claim 1, wherein the image is image processed before the content analysis of the image in step (c).
9. An image enhancement device, comprising:
the setting module is used for selecting a first color space and a second color space to be operated, setting a first parameter range of the first color space and a first image enhancement processing corresponding to the first parameter range, and setting a third parameter range of the second color space and a third image enhancement processing corresponding to the third parameter range;
the analysis module is coupled with the setting module and used for analyzing the content of the image according to the first color space and judging whether the value of at least one color parameter of the image area in the image falls within the first parameter range or not; analyzing the content of the image according to the second color space, and accordingly judging whether the value of at least one color parameter of the image area in the image is within the third parameter range; and
the enhancement module is coupled with the analysis module, if the analysis module judges that the numerical value of at least one color parameter of an image area corresponding to the first color space in the image is in the first parameter range, the enhancement module performs the first image enhancement processing on the image area, and if the analysis module judges that the numerical value of at least one color parameter of an image area corresponding to the second color space in the image is in the third parameter range, the enhancement module performs the third image enhancement processing on the image area corresponding to the second color space;
when the value of the at least one color parameter of the image area is within the first parameter range, the analysis module first determines whether the spatial frequency and the amplitude corresponding to the image area satisfy that the spatial frequency is greater than a preset spatial frequency and the amplitude is less than a preset amplitude, and if so, the enhancement module performs the first image enhancement processing on the image area.
10. The image enhancement device of claim 9, wherein the first color space is an HSG color space, an HSI color space, a YUV color space, an RGB color space or an XYZ color space.
11. The image enhancement device of claim 9, wherein the first image enhancement process performs detail enhancement on the image area to increase the sharpness of the image area.
12. The image enhancement device of claim 9, wherein the setting module further sets a second parameter range of the first color space and a second image enhancement process corresponding to the second parameter range, and the second image enhancement process is different from the first image enhancement process.
13. The image enhancement device of claim 12, wherein if the result of the analysis module is negative, the analysis module further determines whether the value of the at least one color parameter of the image region falls within the second parameter range, and if so, the enhancement module performs the second image enhancement on the image region.
14. The image enhancement device of claim 13, wherein when the value of the at least one color parameter of the image region falls within the second parameter range, the analysis module first determines whether the spatial frequency and the amplitude corresponding to the image region satisfy specific conditions, and if so, the enhancement module performs the second image enhancement on the image region.
15. The image enhancement device of claim 13, wherein if the image region has a value of the at least one color parameter that does not fall within the second parameter range, the analysis module determines that the image region does not need to be enhanced or has noise.
16. The image enhancement device of claim 9, wherein the image is image processed before the analysis module performs content analysis on the image.
CN201710514728.XA 2017-06-29 2017-06-29 Image enhancement method and image enhancement device Expired - Fee Related CN107241590B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN201710514728.XA CN107241590B (en) 2017-06-29 2017-06-29 Image enhancement method and image enhancement device

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN201710514728.XA CN107241590B (en) 2017-06-29 2017-06-29 Image enhancement method and image enhancement device

Publications (2)

Publication Number Publication Date
CN107241590A CN107241590A (en) 2017-10-10
CN107241590B true CN107241590B (en) 2020-03-27

Family

ID=59990922

Family Applications (1)

Application Number Title Priority Date Filing Date
CN201710514728.XA Expired - Fee Related CN107241590B (en) 2017-06-29 2017-06-29 Image enhancement method and image enhancement device

Country Status (1)

Country Link
CN (1) CN107241590B (en)

Families Citing this family (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN108810509B (en) * 2018-07-06 2020-08-11 北京中安未来科技有限公司 Image color correction method and device
CN109640169B (en) 2018-11-27 2020-09-22 Oppo广东移动通信有限公司 Video enhancement control method and device and electronic equipment
CN110507287A (en) * 2019-08-29 2019-11-29 付现敏 A kind of Urology Surgery intelligent digital image processing system and method

Family Cites Families (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US8107762B2 (en) * 2006-03-17 2012-01-31 Qualcomm Incorporated Systems, methods, and apparatus for exposure control
JP4818794B2 (en) * 2006-04-21 2011-11-16 株式会社東芝 Display control apparatus, image processing apparatus, and display control method
CN102113014A (en) * 2008-07-31 2011-06-29 惠普开发有限公司 Perceptual segmentation of images
KR20140121711A (en) * 2013-04-08 2014-10-16 삼성전자주식회사 Method of image proccessing, Computer readable storage medium of recording the method and a digital photographing apparatus
JP6349962B2 (en) * 2014-05-27 2018-07-04 富士ゼロックス株式会社 Image processing apparatus and program

Also Published As

Publication number Publication date
CN107241590A (en) 2017-10-10

Similar Documents

Publication Publication Date Title
CN107241590B (en) Image enhancement method and image enhancement device
JP5095290B2 (en) Shadow region compensation method, medium and system
CN108550158B (en) Image edge processing method, electronic device and computer readable storage medium
CN111739041B (en) Image frame clipping method, device and equipment
JP6052902B2 (en) Methods for processing highlight and saturation areas in digital images
US20060262991A1 (en) Noise reduction method
CN101686306A (en) Visual processing device, visual processing method, visual processing program, integrated circuit, display device, imaging device, and mobile information terminal
JP5212380B2 (en) Image correction apparatus, image correction program, and image correction method
CN109903294B (en) Image processing method and device, electronic equipment and readable storage medium
JP4934454B2 (en) Image reading device
CN112911174A (en) Image dead pixel cluster correction method, computer device and computer readable storage medium
CN110637227B (en) Detection parameter determining method and detection device
JPWO2012004830A1 (en) Image processing device
CN111609998A (en) Detection method and detection device for illumination uniformity and readable storage medium
CN110351549B (en) Screen display state detection method and device, terminal equipment and readable storage medium
CN109345600B (en) Local noise reduction method and device based on tone and terminal
US7885478B2 (en) Noise reduction method and noise reduction apparatus
CN111915497B (en) Image black-and-white enhancement method and device, electronic equipment and readable storage medium
CN111317426A (en) Endoscope parameter self-adaptive adjusting method and device
TWI639975B (en) Image enhancing method and image enhancing apparatus
CN113793277B (en) Image denoising method, device and equipment
KR100406944B1 (en) Method and apparatus for improving printing quality of document in which a text and an image exist together
JP5127627B2 (en) Image processing device
CN113781328A (en) Sigma image filtering method and system
CN111667418A (en) Method and apparatus for image processing

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination
TA01 Transfer of patent application right

Effective date of registration: 20180425

Address after: 200335 E unit 8 building, D building, 207 Songhong Road, Changning District, Shanghai.

Applicant after: BENQ INTELLIGENT TECHNOLOGY (SHANGHAI) CO.,LTD.

Applicant after: BENQ Corp.

Address before: No. 207 Songhong Road, Changning District, Shanghai

Applicant before: BENQ Co.,Ltd.

Applicant before: Benq Corp.

TA01 Transfer of patent application right
GR01 Patent grant
GR01 Patent grant
CF01 Termination of patent right due to non-payment of annual fee

Granted publication date: 20200327

CF01 Termination of patent right due to non-payment of annual fee